The degradation of the catalyst layer represents one of the main limiting factors in a wider adoption of fuel cells. The identification of the contributions of different mechanisms of catalyst degradation, namely the Ostwald ripening and particle agglomeration, is an important step in the development of mitigation strategies for increasing fuel cell reliability and prolonging its life time.
In this paper, the degradation phenomena in high temperature polymer electrolyte membrane fuel cell (HT‐PEMFC) are analyzed using a physically‐based model of fuel cell operation and catalyst degradation, describing carbon corrosion, platinum dissolution and consequent growth of catalyst particles. The model results indicate significantly different time dependence of catalyst particle growth resulting from different mechanisms: linear growth in the case of particle agglomeration and root‐like time dependence for the Ostwald ripening.
Based on these results, a new analytic method is proposed, performed by the fitting of a test root‐function to the time profile of the particle size growth and using best‐fit parameters to identify the prevailing growth mechanism. Using this method on a particle growth time trace deduced from in situ cyclic voltammetry measurement during HT‐PEMFC degradation, we are able to identify the agglomeration as the main mechanism of catalyst particle grow.
The detrimental effects of the catalyst degradation on the overall envisaged lifetime of low-temperature proton-exchange membrane fuel cells (LT-PEMFCs) represent a significant challenge towards further lowering platinum loadings and simultaneously achieving a long cycle life. The elaborated physically based modeling of the degradation processes is thus an invaluable step in elucidating causal interaction between fuel cell design, its operating conditions, and degradation phenomena. However, many parameters need to be determined based on experimental data to ensure plausible simulation results of the catalyst degradation models, which proves to be challenging with the in situ measurements. To fill this knowledge gap, this paper demonstrates the application of a mechanistically based PEMFC modeling framework, comprising real-time capable fuel cell performance, and platinum and carbon support degradation models, to model transient CO2 release rates in the LT-PEMFCs with the consistent calibration of reaction rate parameters under multiple different accelerated stress tests at once. The results confirm the credibility of the physical and chemical modeling basis of the proposed modeling framework, as well as its prediction and extrapolation capabilities. This is confirmed by an increase of only 29% of root mean square deviations values when using a model calibrated on all three data sets at once in comparison to a model calibrated on only one data set. Furthermore, the unique identifiability and interconnection of individual model calibration parameters are determined via Fisher information matrix analysis. This analysis enables optimal reduction of the set of calibration parameters, which results in the speed up of both the calibration process and the general simulation time while retaining the full extrapolation capabilities of the framework.
Polymer electrolyte membrane fuel cells (PEMFCs) are prone to membrane dehydration and liquid water flooding, negatively impacting their performance and lifetime. Therefore, PEMFCs require appropriate water management, which makes accurate water modeling indispensable. Unfortunately, available control-oriented models only replicate individual water-related aspects or use oversimplistic approximations. This paper resolves this challenge by proposing, for the first time, a control-oriented PEMFC stack model focusing on physically motivated water modeling, which covers phase change, liquid water removal, membrane water uptake, and water flooding effects on the electrochemical reaction. Parametrizing the resulting model with measurement data yielded the fitted model. The parameterized model delivers valuable insight into the water mechanisms, which were thoroughly analyzed. In summary, the proposed model enables the derivation of advanced control strategies for efficient water management and mitigation of the degradation phenomena of PEMFCs. Additionally, the model provides the required accuracy for control applications while maintaining the necessary computational efficiency.
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